Making good capital allocation decisions in a volatile market
Allocating capital is a tough job. With market volatility, allocating capital in mining is an especially tough job.
Investors want projects to earn a good return on invested capital with as little risk as possible. So when the market outlook is constantly changing, how do you improve the effectiveness and efficiency of capital allocation in a risky environment? Until we can jump in a time machine and look at the future face-to-face, we need other solutions.
Think about the ways a forecast can vary
The industry accepts that it can’t predict future commodity prices, but everyone still wants their forecasts to be as accurate as possible.
Corporate planning exercises only deal with uncertainty in commodity price forecasts through annual or somewhat more frequent forecast updates and the use of limited corporate forecast scenarios or price decks. Unfortunately, this treatment of forecast uncertainty implies that the current forecast won’t change over the life of the project.
Consider the graph below in which the gold spot price and consensus forecasts are tracked over the past 16 years. The black line shows the spot price in real terms. The blue dots are quarterly long range forecast prices; the left-most blue dot is the long range consensus forecast price for Jan. 1, 2000. The vertical space between the two is the forecast error, and it’s evident that forecast errors can be huge. A reasonable question for executives wanting to improve capital allocation is whether it’s better to put additional resources into making better forecasts, or into describing the variability in forecasts and its impact on capital allocation.
For companies preferring the second approach when dealing with forecast deviation, an integrated valuation and risk modeling (IVRM) framework can help create dynamic cash flow models which recognize forecast uncertainty and our ability to manage it.
Dynamic cash flow models constructed within the IVRM framework can improve the quality of capital allocation decisions in two important ways:
1 Improve the analytical description of the capital allocation problem
Static cash flow models provide an incomplete description of a capital allocation problem which may limit our understanding and choice of solutions. IVRM improves our understanding of the decision by explicitly modeling long term forecast uncertainty and how we can manage it. With IVRM, variability in long term price forecasts are no longer treated as errors but instead become part of the problem description along with potential management solutions.
2 Reduce cash flow estimation errors and communicate risk exposure
Cash flow estimation errors come into play when you ignore forecast uncertainty due to the “flaw of averages”. The IVRM framework reduces estimation errors because the cash flow effects of forecast uncertainty are quantified. Explicitly modeling forecast uncertainty also provides additional tools to measure and communicate risk exposure that are more powerful than simple sensitivity analysis used with conventional cash flow analysis.
As far as results go, the benefits are easy to identify. In one example, an investment firm used IVRM methods to renegotiate a sliding scale royalty while the mine owner used a conventional static analysis. The mine owner was happy with the new terms but their analysis didn’t reflect the value of the royalty increased with the changes. Unfortunately, their static analysis had ignored the possibility of higher royalty rates if metal prices increased. The investment company found an IVRM approach a distinct advantage in their negotiation as it allowed them to assess the revised royalty terms in both high and low price metal environments.
Amidst all this uncertainty investors are demanding that mining companies improve the effectiveness of their capital allocation decisions. To deliver that, companies need to consider their investment in a range of economic conditions and how their investments will perform in these situations. With IVRM, mining professionals can improve the efficiency of their efforts by accounting for the risk created by forecast uncertainty.
Michael Samis is an EY associate partner, Transaction Advisory Services.